# Image-Text Generation

A quantized version of the Qwen2.5-VL-72B-Instruct multimodal large language model, supporting image-text-to-text tasks, suitable for various quantization levels from high precision to low memory requirements.

Qwen2.5-VL-7B-Instruct is a multimodal model based on the Qwen2.5 architecture, supporting joint processing of images and text, suitable for vision-language tasks.

UI-TARS-1.5-7B-4bit is a multimodal model focused on image-text-to-text conversion tasks, supporting the English language.

InternVL3 is an advanced series of multimodal large language models, demonstrating exceptional multimodal perception and reasoning capabilities, supporting image, video, and text inputs.

A multimodal model trained based on google/gemma-3-4b-it, specializing in high-quality data processing for mathematics, programming, science, and puzzle-solving domains.

INT4 quantized version based on the gemma-3-4b-it model, significantly reducing storage and computational resource requirements

A vision-language model based on the Qwen2.5-VL-7B-Instruct model with GPTQ-INT8 quantization

Gemma 3 is Google's lightweight open-source multimodal model series based on Gemini technology, supporting text and image inputs with text outputs. The 12B version undergoes instruction tuning and quantization-aware training (QAT), making it suitable for deployment in resource-limited environments.

VoRA is a vision-language model based on 7B parameters, focusing on image-text-to-text conversion tasks.

VoRA is a vision-language model based on 7B parameters, capable of processing image and text inputs to generate text outputs.

This is the GGUF quantized version of the Qwen2.5-VL-7B-Instruct model, suitable for multimodal tasks and supports both image and text inputs.

Qwen2.5-VL-7B-Instruct is a multimodal vision-language model that supports image understanding and text generation tasks.

A Japanese visual language model trained based on the NVILA-Lite architecture, supporting image-text interaction in both Japanese and English

Qwen2.5-VL-72B-Instruct is a large-scale vision-language model developed by the Tongyi Qianwen team, supporting multimodal understanding and generation of images and text.

Gemma 3 is a lightweight open model series launched by Google, built on the same technology as Gemini, supporting multimodal input and text output.

Meta's Chameleon series 7B-parameter multimodal model supporting image-text-to-text tasks

The static quantized version of ToriiGate-v0.4-7B, suitable for multimodal, vision, and image-text-to-text tasks

InternVL2_5-4B-AWQ is the AWQ quantized version of InternVL2_5-4B using autoawq, supporting multilingual and multimodal tasks.

Gemma is a lightweight, advanced open model series launched by Google, built on the same research and technology as Gemini. Gemma 3 is a multimodal model capable of processing both text and image inputs to generate text outputs.

Qwen2-VL-2B-Instruct is a multimodal vision-language model that supports interaction between images and text, suitable for image understanding and generation tasks.

Qwen2-VL-7B-Instruct is a multimodal vision-language model that supports joint understanding and generation tasks for images and text.

MiniVLA is a 1-billion-parameter vision-language model compatible with the Prismatic Vision-Language Model codebase, suitable for robotics and multimodal tasks.

p-MoD is a hybrid-depth multimodal large language model built using the progressive ratio decay method, supporting image-to-text generation tasks.

PaliGemma 2 is a vision-language model (VLM) that combines the capabilities of the Gemma 2 model. It can process both image and text inputs simultaneously and generate text outputs, supporting multiple languages. It is suitable for various vision-language tasks such as image and short video captioning, visual question answering, text reading, object detection, and object segmentation.

PaliGemma 2 is a vision-language model based on Gemma 2, supporting image and text input to generate text output, suitable for various vision-language tasks.

xGen-MM is a series of the latest foundational large multimodal models (LMMs) developed by Salesforce AI Research, building upon the successful design of the BLIP series with foundational enhancements to ensure a more robust and superior model foundation.

LLaVA-MORE is an enhanced version based on the LLaVA architecture, integrating LLaMA 3.1 as the language model, focusing on image-to-text tasks.

This project trained a projection module to add visual capabilities to Llama 3 using SigLIP technology, applied to the Llama-3.1-8B-Instruct model.

Florence-2 is an advanced visual foundation model developed by Microsoft, employing a prompt-based approach to handle a wide range of vision and vision-language tasks.

Florence-2 is an advanced vision foundation model developed by Microsoft, employing a prompt-based approach to handle a wide range of vision and vision-language tasks.

PaliGemma is a versatile, lightweight vision-language model (VLM) built upon the SigLIP vision model and Gemma language model, supporting image and text inputs with text outputs.

This model is a GGUF format conversion based on xtuner/llava-llama-3-8b-v1_1, supporting multimodal processing of images and text.

This model is a GGUF format version converted from xtuner/llava-llama-3-8b-v1_1, suitable for the llama.cpp framework, supporting image-text-to-text conversion tasks.

This model is a GGUF format conversion based on xtuner/llava-llama-3-8b-v1_1, supporting multimodal interaction between images and text.

MoAI is a large-scale language and vision hybrid model capable of processing both image and text inputs to generate text outputs.

LLaVA is an open-source multimodal chatbot trained by fine-tuning LLM on multimodal instruction-following data. This version is the GGUF quantized version, offering multiple quantization options.

LLaVA is an open-source multimodal chatbot, fine-tuned on LLaMA/Vicuna and trained with GPT-generated multimodal instruction-following data.

BLIP is a unified vision-language pretraining framework, excelling in tasks like image caption generation, supporting both conditional and unconditional text generation

IDEFICS-9B is a 9-billion-parameter multimodal model capable of processing both image and text inputs to generate text outputs. It is an open-source replication of Deepmind's Flamingo model.